The PADME Detector

Gianotti, P.

doi:10.1051/epjconf/201817001007

Cited by 4 publications

(1 citation statement)

References 6 publications

(5 reference statements)

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“…The targeting type places the pixel sensor facing the incoming beam. The particles in the beam penetrate the pixel sensor and generate charges collected by the electrodes [9]. Generally, the targeting type has rigorous demands on the radiation hardening, and in most cases the incoming particles are light-weighted, because the deposition of heavy ions will cause the malfunction of the sensor.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach to multi-track location and orientation in gaseous drift chambers

Ai,

Wang,

Sun

et al. 2020

Preprint

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Accurate measuring the location and orientation of individual particles in a beam monitoring system is of particular interest to researchers in multiple disciplines. Among feasible methods, gaseous drift chambers with hybrid pixel sensors have the great potential to realize long-term stable measurement with considerable precision. In this paper, we introduce deep learning to analyze patterns in the beam projection image to facilitate three-dimensional reconstruction of particle tracks. We propose an end-to-end neural network based on segmentation and fitting for feature extraction and regression. Two segmentation heads, named binary segmentation and semantic segmentation, perform initial track determination and pixel-track association. Then pixels are assigned to multiple tracks, and a weighted least squares fitting is implemented with full back-propagation. Besides, we introduce a center-angle measure to judge the precision of location and orientation by combining two separate factors. The initial position resolution could achieve 8.8 µm for the single track and 11.4 µm (15.2 µm) for the 1-3 tracks (1-5 tracks), and the angle resolution could achieve 0.15 • and 0.21 • (0.29 • ) respectively. These results show a significant improvement in accuracy and multi-track compatibility compared to traditional methods.

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Section: Introductionmentioning

confidence: 99%

A deep learning approach to multi-track location and orientation in gaseous drift chambers

Ai,

Wang,

Sun

et al. 2020

Preprint

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A deep learning approach to multi-track location and orientation in gaseous drift chambers

Wang

Sun

et al. 2020

Nuclear Instruments and Methods in Physics Research Section A:

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The Limadou-HEPD Segmented Calorimeter

Vitale

2019

EPJ Web Conf.

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The core of the High-Energy Particle Detector (HEPD) on board of the China Seismo-Electromagnetic Satellite (CSES) is a segmented calorimeter, which is composed with an upper tower of plastic scintillator counters and a bottom array of LYSO large crystals. Electrons with energy below 100MeV, protons and light nuclei, below few hundreds ofMeV/nucleon are fully contained within this calorimeter. Mainly the LYSO array (density 7.3 g/cm3, thickness around 29.2 g/cm2) extends the HEPD energy range, allowing those measurements (solar energetic particles, low-energy cosmic rays) which are more related to astroparticle physics topics. Two identical copies of HEPD, and then of its calorimeter, exist: the Flight (FM) and the Qualification (QM) models. While the FM has achieved the orbit on board of the CSES satellite in February 2018, the Qualification Model, is used, at ground, for tests and calibrations. A report on the characterization of this compact particle space detector and on preliminary studies and results, will be given.

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The PADME Detector

Cited by 4 publications

References 6 publications

A deep learning approach to multi-track location and orientation in gaseous drift chambers

A deep learning approach to multi-track location and orientation in gaseous drift chambers

A deep learning approach to multi-track location and orientation in gaseous drift chambers

The Limadou-HEPD Segmented Calorimeter

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